To perform 3D object detection on the BEV feature grid, we adopt the backbone and detection head of the well-established BEV 3D object detector PointPillars [1]}, as it has been shown to provide accurate 3D detection results with a low computational overhead. For the BEV backbone, we increase the number of 3x3 convolution + BatchNorm + ReLU layers in the downsample blocks from (4, 6, 6) used in the original PointPillars [1]} to (10, 10, 10) for Block1, Block2, and Block3 respectively. Increasing the number of convolutional layers expands the learning capacity in our BEV network, important for learning from lower quality features produced by images compared to higher quality features originally produced by LiDAR point clouds. We use the same detection head as PointPillars [1]} to generate our final detections.
